Design And Research Of Image Acquisition And Enhancement System For Small UAV

The acquisition of traditional crop images is mainly based on manual collection,which is not conducive to large-scale operations.The drone's method of obtaining crop images is mainly applied in the field of aerial remote sensing.Meanwhile,the quality of crop images obtained by man-made and unmanned aerial vehicles are not high,which is not conducive to image segmentation and recognition.It is urgent to design a real-time image acquisition and enhancement system for UAV,combined with video image processing technology and electronic information technology.In this research,an image acquisition and enhancement system based on UAV was designed.A four-axis small UAV was used as the image acquisition platform and Raspberry Pi processor as the hardware core,and WiFi wireless communication technology and extended UDP transmission protocol are used to realize the wireless transmission of image data.Aiming at the problem of noise in the process of collection and transmission of image data,a wavelet decomposition denoising image was designed based on the noise level of the image using different wavelet basis functions.For the camera module,because of the vibration of the drone caused by the drone shaking and the relative movement between the process of the image acquisition and the target scene caused by the drone,resulting in the appearance of the image blurring,an improved Lucy-Richardson(L-R)algorithm was proposed to deblur the image.For the phenomenon that the overall image of the drone image collected by the drone in the rain and fog weather is gray,the CLAHE algorithm was used to achieve image dehazing.The main work of this research was as follows:(1)Three different regions were selected to test the signal intensity,effective communication distance and network packet loss rate of the wireless LAN.The experimental results showed that the effective communication distance of the wireless network in the open area(field area)was 150 m,the signal intensity was stable in-60 dBm to-40 dBm,and the packet loss rate was less than 6.5%.(2)The image data of the system were compressed and decode using the H.264 codec algorithm.The test results of this research showed that the compression rate of the image data based on the H.264 codec algorithm was 92% to 96%.(3)The process of image enhancement algorithm was that the first step was to denoised the image by using different wavelet based functions according to the noise level of the image.When the noise level was low,the sym8 wavelet based function was used for wavelet decomposition.When the noise level was high,the dior2.4 wavelet based function was used for wavelet decomposition.The second step was to use the improved L-R algorithm to achieve image deblurring.The third step used adaptive threshold to segment the sky region of the image,and then used the CLAHE algorithm to achieved image dehazing.(4)For the defect of the LR algorithm,that is,the degree of pollution was positively related to the number of iterations,the more times the ringing effect was more serious.In order to weaken the ringing effect in the iterative process,a improved L-R algorithm was designed by introducing the gain concept in the original L-R algorithm.(5)Wiener filtering,constrained least squares filtering and original LR algorithm were used to verify the feasibility of the improved L-R algorithm using three different image motion blur algorithms.Using the dark channel prior algorithm and the multi-scale Retinex algorithm,these two different image dehazing algorithms verified the advanced nature of the CLAHE algorithm.The system can processed a frame of image data(resolution 320×240)with 31.63 ms from the time of image data acquisition to the user terminal receiving,the image transmission rate can reach 32 fps,which can meet the real-time requirements of the system.The structural image similarity(SSIM)values of the enhanced image data were all above 0.85,and the image information entropy was greatly improved.When the resolution of the image data was 640×480 or less,the success rate of the system sending image data was above 90%,the transmission rate was greater than 1.5Mbps.The experiment of this research showed that in order to improve the real-time and reliability of the system work,the system should set the resolution of the image to 640×480 or less when acquiring the image data.The system constructed in this research can be applied to image collection of large-scale and high-efficiency crops,which provides high quality image data for the later image segmentation and image recognition and can also serve as a reference and reference for related R&D systems in agricultural and industrial production monitoring.